Symmetry and Data Augmentation

CHANGELOG.md

@@ -13,14 +13,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - `identify_non_dominated_configurations` method to `Campaign` and `Objective`
   for determining the Pareto front
 - Interpoint constraints for continuous search spaces
+- Symmetry classes (`PermutationSymmetry`, `MirrorSymmetry`, `DependencySymmetry`)
+  for expressing invariances and configuring surrogate data augmentation
+- `Parameter.is_equivalent` method for structural parameter comparison
 
 ### Breaking Changes
 - `ContinuousLinearConstraint.to_botorch` now returns a collection of constraint tuples
   instead of a single tuple (needed for interpoint constraints)
+- `df_apply_permutation_augmentation` has a different interface and now expects
+  permutation groups instead of column groups
 
 ### Fixed
 - `SHAPInsight` breaking with `numpy>=2.4` due to no longer accepted implicit array to 
   scalar conversion
+- `DiscretePermutationInvarianceConstraint` no longer erroneously removes diagonal
+  points (e.g., where all permuted parameters have the same value)
 
 ### Changed
 - The `Campaign.allow_*` flag mechanism is now based on `AutoBool` logic, providing

baybe/constraints/base.py

@@ -37,10 +37,6 @@ class Constraint(ABC, SerialMixin):
     eval_during_modeling: ClassVar[bool]
     """Class variable encoding whether the condition is evaluated during modeling."""
 
-    eval_during_augmentation: ClassVar[bool] = False
-    """Class variable encoding whether the constraint could be considered during data
-    augmentation."""
-
     numerical_only: ClassVar[bool] = False
     """Class variable encoding whether the constraint is valid only for numerical
     parameters."""

baybe/constraints/conditions.py

@@ -95,7 +95,7 @@ class Condition(ABC, SerialMixin):
     """Abstract base class for all conditions.
 
     Conditions always evaluate an expression regarding a single parameter.
-    Conditions are part of constraints, a constraint can have multiple conditions.
+    Conditions are part of constraints and symmetries.
     """
 
     @abstractmethod

baybe/constraints/discrete.py

@@ -29,6 +29,9 @@
 if TYPE_CHECKING:
     import polars as pl
 
+    from baybe.symmetries.dependency import DependencySymmetry
+    from baybe.symmetries.permutation import PermutationSymmetry
+
 
 @define
 class DiscreteExcludeConstraint(DiscreteConstraint):
@@ -195,10 +198,6 @@ class DiscreteDependenciesConstraint(DiscreteConstraint):
     a single constraint.
     """
 
-    # class variables
-    eval_during_augmentation: ClassVar[bool] = True
-    # See base class
-
     # object variables
     conditions: list[Condition] = field()
     """The list of individual conditions."""
@@ -271,39 +270,56 @@ def get_invalid(self, data: pd.DataFrame) -> pd.Index:
 
         return inds_bad
 
+    def to_symmetries(
+        self, use_data_augmentation: bool = True
+    ) -> tuple[DependencySymmetry, ...]:
+        """Convert to :class:`~baybe.symmetries.dependency.DependencySymmetry` objects.
+
+        Create one symmetry object per dependency relationship, i.e., per
+        (parameter, condition, affected_parameters) triple.
+
+        Args:
+            use_data_augmentation: Flag indicating whether the resulting symmetry
+                objects should apply data augmentation. ``True`` means that
+                measurement augmentation will be performed by replacing inactive
+                affected parameter values with all possible values.
+
+        Returns:
+            A tuple of dependency symmetries, one for each dependency in the
+            constraint.
+        """
+        from baybe.symmetries.dependency import DependencySymmetry
+
+        return tuple(
+            DependencySymmetry(
+                parameter_name=p,
+                condition=c,
+                affected_parameter_names=aps,
+                use_data_augmentation=use_data_augmentation,
+            )
+            for p, c, aps in zip(
+                self.parameters, self.conditions, self.affected_parameters, strict=True
+            )
+        )
+
 
 @define
 class DiscretePermutationInvarianceConstraint(DiscreteConstraint):
     """Constraint class for declaring that a set of parameters is permutation invariant.
 
     More precisely, this means that, ``(val_from_param1, val_from_param2)`` is
-    equivalent to ``(val_from_param2, val_from_param1)``. Since it does not make sense
-    to have this constraint with duplicated labels, this implementation also internally
-    applies the :class:`baybe.constraints.discrete.DiscreteNoLabelDuplicatesConstraint`.
+    equivalent to ``(val_from_param2, val_from_param1)``.
 
     *Note:* This constraint is evaluated during creation. In the future it might also be
     evaluated during modeling to make use of the invariance.
     """
 
-    # class variables
-    eval_during_augmentation: ClassVar[bool] = True
-    # See base class
-
     # object variables
     dependencies: DiscreteDependenciesConstraint | None = field(default=None)
     """Dependencies connected with the invariant parameters."""
 
     @override
     def get_invalid(self, data: pd.DataFrame) -> pd.Index:
-        # Get indices of entries with duplicate label entries. These will also be
-        # dropped by this constraint.
-        mask_duplicate_labels = pd.Series(False, index=data.index)
-        mask_duplicate_labels[
-            DiscreteNoLabelDuplicatesConstraint(parameters=self.parameters).get_invalid(
-                data
-            )
-        ] = True
-
         # Merge a permutation invariant representation of all affected parameters with
         # the other parameters and indicate duplicates. This ensures that variation in
         # other parameters is also accounted for.
@@ -314,20 +330,14 @@ def get_invalid(self, data: pd.DataFrame) -> pd.Index:
                 data[self.parameters].apply(cast(Callable, frozenset), axis=1),
             ],
             axis=1,
-        ).loc[
-            ~mask_duplicate_labels  # only consider label-duplicate-free part
-        ]
+        )
         mask_duplicate_permutations = df_eval.duplicated(keep="first")
 
-        # Indices of entries with label-duplicates
-        inds_duplicate_labels = data.index[mask_duplicate_labels]
-
-        # Indices of duplicate permutations in the (already label-duplicate-free) data
-        inds_duplicate_permutations = df_eval.index[mask_duplicate_permutations]
+        # Indices of duplicate permutations
+        inds_invalid = data.index[mask_duplicate_permutations]
 
         # If there are dependencies connected to the invariant parameters evaluate them
         # here and remove resulting duplicates with a DependenciesConstraint
-        inds_invalid = inds_duplicate_labels.union(inds_duplicate_permutations)
         if self.dependencies:
             self.dependencies.permutation_invariant = True
             inds_duplicate_independency_adjusted = self.dependencies.get_invalid(
@@ -337,6 +347,32 @@ def get_invalid(self, data: pd.DataFrame) -> pd.Index:
 
         return inds_invalid
 
+    def to_symmetry(self, use_data_augmentation: bool = True) -> PermutationSymmetry:
+        """Convert to a :class:`~baybe.symmetries.permutation.PermutationSymmetry`.
+
+        The constraint's parameters form the primary permutation group. If
+        dependencies are attached, their parameters are added as an additional
+        group that is permuted in lockstep.
+
+        Args:
+            use_data_augmentation: Flag indicating whether the resulting symmetry
+                object should apply data augmentation. ``True`` means that
+                measurement augmentation will be performed by generating all
+                permutations of parameter values within each group.
+
+        Returns:
+            The corresponding permutation symmetry.
+        """
+        from baybe.symmetries.permutation import PermutationSymmetry
+
+        groups = [self.parameters]
+        if self.dependencies:
+            groups.append(list(self.dependencies.parameters))
+        return PermutationSymmetry(
+            permutation_groups=groups,
+            use_data_augmentation=use_data_augmentation,
+        )
+
 
 @define
 class DiscreteCustomConstraint(DiscreteConstraint):

baybe/parameters/base.py

@@ -7,6 +7,7 @@
 from functools import cached_property
 from typing import TYPE_CHECKING, Any, ClassVar
 
+import attrs
 import pandas as pd
 from attrs import define, field
 from attrs.converters import optional as optional_c
@@ -88,6 +89,22 @@ def to_searchspace(self) -> SearchSpace:
 
         return SearchSpace.from_parameter(self)
 
+    def is_equivalent(self, other: Parameter) -> bool:
+        """Check if this parameter is equivalent to another, ignoring the name.
+
+        Two parameters are considered equivalent if they have the same type and
+        all attributes are equal except for the name.
+
+        Args:
+            other: The parameter to compare against.
+
+        Returns:
+            ``True`` if the parameters are equivalent, ``False`` otherwise.
+        """
+        if type(self) is not type(other):
+            return False
+        return attrs.evolve(self, name=other.name) == other
+
     @abstractmethod
     def summary(self) -> dict:
         """Return a custom summarization of the parameter."""

baybe/parameters/categorical.py

@@ -13,13 +13,7 @@
 from baybe.parameters.enum import CategoricalEncoding
 from baybe.parameters.validation import validate_unique_values
 from baybe.settings import active_settings
-from baybe.utils.conversion import nonstring_to_tuple
-
-
-def _convert_values(value, self, field) -> tuple[str, ...]:
-    """Sort and convert values for categorical parameters."""
-    value = nonstring_to_tuple(value, self, field)
-    return tuple(sorted(value, key=lambda x: (str(type(x)), x)))
+from baybe.utils.conversion import normalize_convertible2str_sequence
 
 
 def _validate_label_min_len(self, attr, value) -> None:
@@ -38,8 +32,10 @@ class CategoricalParameter(_DiscreteLabelLikeParameter):
     # object variables
     _values: tuple[str | bool, ...] = field(
         alias="values",
-        converter=Converter(_convert_values, takes_self=True, takes_field=True),  # type: ignore
-        validator=(
+        converter=Converter(  # type: ignore[misc,call-overload]  # mypy: Converter
+            normalize_convertible2str_sequence, takes_self=True, takes_field=True
+        ),
+        validator=(  # type: ignore[arg-type]  # mypy: validator tuple
             validate_unique_values,
             deep_iterable(
                 member_validator=(instance_of((str, bool)), _validate_label_min_len),

baybe/parameters/numerical.py

@@ -13,9 +13,10 @@
 
 from baybe.exceptions import NumericalUnderflowError
 from baybe.parameters.base import ContinuousParameter, DiscreteParameter
-from baybe.parameters.validation import validate_is_finite, validate_unique_values
+from baybe.parameters.validation import validate_unique_values
 from baybe.settings import active_settings
 from baybe.utils.interval import InfiniteIntervalError, Interval
+from baybe.utils.validation import validate_is_finite
 
 
 @define(frozen=True, slots=False)

baybe/parameters/validation.py

@@ -1,9 +1,7 @@
 """Validation functionality for parameters."""
 
-from collections.abc import Sequence
 from typing import Any
 
-import numpy as np
 from attrs.validators import gt, instance_of, lt
 
 
@@ -28,18 +26,3 @@ def validate_decorrelation(obj: Any, attribute: Any, value: float) -> None:
     if isinstance(value, float):
         gt(0.0)(obj, attribute, value)
         lt(1.0)(obj, attribute, value)
-
-
-def validate_is_finite(  # noqa: DOC101, DOC103
-    obj: Any, _: Any, value: Sequence[float]
-) -> None:
-    """Validate that ``value`` contains no infinity/nan.
-
-    Raises:
-        ValueError: If ``value`` contains infinity/nan.
-    """
-    if not all(np.isfinite(value)):
-        raise ValueError(
-            f"Cannot assign the following values containing infinity/nan to "
-            f"parameter {obj.name}: {value}."
-        )

baybe/recommenders/pure/bayesian/base.py

@@ -86,6 +86,22 @@ def _get_acquisition_function(self, objective: Objective) -> AcquisitionFunction
             return qLogNEHVI() if objective.is_multi_output else qLogEI()
         return self.acquisition_function
 
+    def _get_surrogate_for_augmentation(self) -> Surrogate | None:
+        """Get the Surrogate instance for augmentation/validation, if available."""
+        from baybe.surrogates.composite import CompositeSurrogate, _ReplicationMapping
+
+        model = self._surrogate_model
+        if isinstance(model, Surrogate):
+            return model
+        if isinstance(model, CompositeSurrogate):
+            # All inner surrogates are copies of the same template
+            surrogates = model.surrogates
+            if isinstance(surrogates, _ReplicationMapping):
+                template = surrogates.template
+                if isinstance(template, Surrogate):
+                    return template
+        return None
+
     def get_surrogate(
         self,
         searchspace: SearchSpace,
@@ -114,6 +130,13 @@ def _setup_botorch_acqf(
                 f"{len(objective.targets)}-target multi-output context."
             )
 
+        # Perform data augmentation if configured
+        surrogate_for_augmentation = self._get_surrogate_for_augmentation()
+        if surrogate_for_augmentation is not None:
+            measurements = surrogate_for_augmentation.augment_measurements(
+                measurements, searchspace.parameters
+            )
+
         surrogate = self.get_surrogate(searchspace, objective, measurements)
         self._botorch_acqf = acqf.to_botorch(
             surrogate,
@@ -156,6 +179,13 @@ def recommend(
 
         validate_object_names(searchspace.parameters + objective.targets)
 
+        # Validate compatibility of surrogate symmetries with searchspace
+        surrogate_for_validation = self._get_surrogate_for_augmentation()
+        if surrogate_for_validation is not None:
+            for s in surrogate_for_validation.symmetries:
+                s.validate_searchspace_context(searchspace)
+
+        # Experimental input validation
         if (measurements is None) or measurements.empty:
             raise NotImplementedError(
                 f"Recommenders of type '{BayesianRecommender.__name__}' do not support "

baybe/searchspace/core.py

@@ -381,11 +381,6 @@ def transform(
 
         return comp_rep
 
-    @property
-    def constraints_augmentable(self) -> tuple[Constraint, ...]:
-        """The searchspace constraints that can be considered during augmentation."""
-        return tuple(c for c in self.constraints if c.eval_during_augmentation)
-
     def get_parameters_by_name(self, names: Sequence[str]) -> tuple[Parameter, ...]:
         """Return parameters with the specified names.